Infertility has been reported to affect as many as 2.75 million couples in the United States. It is estimated that in half of these, the male may be responsible for or contribute to the problem. The purpose of this study is to evaluate the effect which past sexual practices, reproductive history, contraceptive use, personal habits, medical and surgical history, and occupational exposures have on the occurrence of undesired infertility in men and women. To accomplish this, data from a recent multi-center case-control study on birth control practices and subsequent undesired infertility are available. This initial study enrolled all eligible couples who sought medical assistance for infertility at one of seven collaborating centers between January 1, 1981 and May 31, 1983. Interviews of 1,880 infertile women and 4,023 delivery controls were conducted. The controls were women who were admitted for delivery of a livebirth at the participating institutions between January 1, 1981 and August 31, 1983. They were matched to the cases on geographic location, age, race, marital status and pay status. In addition, the 1,880 partners of the infertile women underwent medical evaluation, including semen analysis, and 77% of them agreed to complete a self-administered questionnaire. Men with abnormal semen analysis results will be compared to those whose test results were normal. The cases will be analyzed separately according to the major source of the infertility problem: cervical factor, tubal factor, ovulatory failure, endometriosis, or male factor. The specific exposures which will be investigated for their association with each type of infertility include in utero diethylstilbesterol exposure; occupational exposure to chemicals, pesticides, fumes, heat, and radiation; prior pelvic surgery, abortion, and ectopic pregnancy; smoking history; alcohol and caffeine use; prescription and non-prescription drug use; marijuana use; douching habits; and vigorous exercise. Each exposure will be evaluated using multivariate logistic regression to estimate any increase in risk while controlling for other known risk factors and for potential confounding factors such as age, region, religion, and education.